Abstract

Train becomes one of the most popular mass transportation media in Indonesia, especially for medium and long
distance journey. When holiday comes, PT. KAI (persero) need to make much additional departures of train in order to
occupy the passanggers need. From this statement, risen the ideas to design a forecasting system to forecast the number
of train’s passanger using artificial neural network and to analyze the neural network which gives the best performance
in this system.
This final project was designed a forecasting system to predict the number of train’s passanger using Back
Propagation Neural Network. First step, all data need to be computerized and divided them into 2 groups, 60% for
training data and 40% for testing data. When training the neural network, training data is used as the input and it
process will stops when the values of required training parameters is reached. Testing the neural network can be done
using training data and testing data. This process is aimed for knowing network’s performance by comparing
simulation results with real target. Neural network with best performance can be used for forecasting process.
Based on training data test results, the best performance neural network given by the network with 10 nodes of
hidden layer for variation increasing the number of node of hidden layer and the network with its learning rate was
0,01 for variation decreasing the value of learning rate. Based on testing data test results, the best performance neural
network given by the network with 5 nodes of hidden layer for variation increasing the number of node of hidden layer
and the network with its learning rate was 0,001 for variation decreasing the value of learning rate
Keywords : forecasting, train’s passanger, back propagation